#
# Copyright (C) 2023, Inria
# GRAPHDECO research group, https://team.inria.fr/graphdeco
# All rights reserved.
#
# This software is free for non-commercial, research and evaluation use 
# under the terms of the LICENSE.md file.
#
# For inquiries contact  george.drettakis@inria.fr
#

import os
import torch
import numpy as np
from random import randint
from utils.loss_utils import l1_loss, ssim, first_order_edge_aware_loss
from utils.sh_utils import RGB2SH, eval_sh
from gaussian_renderer import render, network_gui
import sys
import torch.nn.functional as F
from scene import Scene, GaussianModel
from utils.general_utils import safe_state
from bvh import RayTracer
from tqdm import tqdm
from utils.image_utils import psnr
from argparse import ArgumentParser, Namespace
from arguments import ModelParams, PipelineParams, OptimizationParams
from torchvision.utils import save_image
try:
    from torch.utils.tensorboard import SummaryWriter
    TENSORBOARD_FOUND = True
except ImportError:
    TENSORBOARD_FOUND = False
    
normalize = lambda x: (x - x.min()) / (x.max() - x.min())

def training(dataset, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from):
    first_iter = 0
    tb_writer = prepare_output_and_logger(dataset)
    gaussians = GaussianModel(dataset.sh_degree)
    scene = Scene(dataset, gaussians)
    gaussians.training_setup(opt)
    if checkpoint:
        (model_params, first_iter) = torch.load(checkpoint)
        gaussians.restore(model_params, opt)

    bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
    background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")

    iter_start = torch.cuda.Event(enable_timing = True)
    iter_end = torch.cuda.Event(enable_timing = True)

    viewpoint_stack = None
    ema_loss_for_log = 0.0
    progress_bar = tqdm(range(first_iter, opt.iterations), desc="Training progress")
    first_iter += 1
    for iteration in range(first_iter, opt.iterations + 1):        
        iter_start.record()

        gaussians.update_learning_rate(iteration)

        # Every 1000 its we increase the levels of SH up to a maximum degree
        if iteration % 1000 == 0:
            gaussians.oneupSHdegree()

        # Pick a random Camera
        if not viewpoint_stack:
            viewpoint_stack = scene.getTrainCameras().copy()
        viewpoint_cam = viewpoint_stack.pop(randint(0, len(viewpoint_stack)-1))

        # Render
        if (iteration - 1) == debug_from:
            pipe.debug = True

        bg = torch.rand((3), device="cuda") if opt.random_background else background

        render_pkg = render(viewpoint_cam, gaussians, pipe, bg)
        image, viewspace_point_tensor, visibility_filter, radii = \
            render_pkg["render"], render_pkg["viewspace_points"], render_pkg["visibility_filter"], render_pkg["radii"]

        
        depth = render_pkg["depth"].repeat(3,1,1)
        rendered_normal = render_pkg["rendered_normal"]
        pseudo_normal = render_pkg["pseudo_normal"]
        
        # Loss
        gt_image = viewpoint_cam.original_image.cuda()
        img_Ll1 = F.l1_loss(image, gt_image)
        img_loss = (1.0 - opt.lambda_dssim) * img_Ll1 + opt.lambda_dssim * (1.0 - ssim(image, gt_image))
        normal_Ll2 = F.mse_loss(rendered_normal, pseudo_normal.detach())
        normal_tv_loss = first_order_edge_aware_loss(rendered_normal, gt_image)
        normal_loss = 0.8 * normal_Ll2 + 0.2 * normal_tv_loss
        
        loss = 0.9 * img_loss + 0.1 * normal_loss
        
        loss.backward()

        iter_end.record()
        
        if iteration % 1000 == 0:
            vis_dir = os.path.join(dataset.model_path, 'vis')
            os.makedirs(vis_dir, exist_ok=True)
            img = torch.cat([image, gt_image, normalize(depth), pseudo_normal, rendered_normal], -1)
            save_image(img, os.path.join(vis_dir, f"iters_{iteration:04d}.png"))


        with torch.no_grad():
            # Progress bar
            ema_loss_for_log = 0.4 * loss.item() + 0.6 * ema_loss_for_log
            if iteration % 10 == 0:
                progress_bar.set_postfix({"Loss": f"{ema_loss_for_log:.6f}", 
                                          "PSNR": f"{psnr(gt_image, image).mean():.2f}",
                                          "Pts": f"{gaussians.get_xyz.shape[0]}"})
                progress_bar.update(10)
            if iteration == opt.iterations:
                progress_bar.close()

            # Log and save
            training_report(tb_writer, iteration, img_Ll1, loss, l1_loss, iter_start.elapsed_time(iter_end), testing_iterations, scene, render, (pipe, background))
            if (iteration in saving_iterations):
                print("\n[ITER {}] Saving Gaussians".format(iteration))
                scene.save(iteration)

            # Densification
            if iteration < opt.densify_until_iter:
                # Keep track of max radii in image-space for pruning
                gaussians.max_radii2D[visibility_filter] = torch.max(gaussians.max_radii2D[visibility_filter], radii[visibility_filter])
                gaussians.add_densification_stats(viewspace_point_tensor, visibility_filter)

                if iteration > opt.densify_from_iter and iteration % opt.densification_interval == 0:
                    size_threshold = 20 if iteration > opt.opacity_reset_interval else None
                    gaussians.densify_and_prune(opt.densify_grad_threshold, 0.005, scene.cameras_extent, size_threshold)
                
                if iteration % opt.opacity_reset_interval == 0 or (dataset.white_background and iteration == opt.densify_from_iter):
                    gaussians.reset_opacity()

            # Optimizer step
            if iteration < opt.iterations:
                gaussians.optimizer.step()
                gaussians.optimizer.zero_grad(set_to_none = True)

            if (iteration in checkpoint_iterations):
                print("\n[ITER {}] Saving Checkpoint".format(iteration))
                torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
                
    train_visibility(viewpoint_cam, gaussians, pipe, bg)
    
    
def train_visibility(viewpoint_cam, gaussians, pipe, bg, iterations=10000):
    gaussians._xyz.requires_grad = False
    gaussians._normal.requires_grad = False
    gaussians._opacity.requires_grad = False
    gaussians._scaling.requires_grad = False
    gaussians._rotation.requires_grad = False
    
    visibility_sh_lr = 1e-2
    optimizer = torch.optim.Adam([
        {'params': [gaussians._features_dc], 'lr': visibility_sh_lr},
        {'params': [gaussians._features_rest], 'lr': visibility_sh_lr / 20}
    ])
    
    means3D = gaussians.get_xyz
    opacity = gaussians.get_opacity[:, 0]
    scaling = gaussians.get_scaling
    rotation = gaussians.get_rotation
    normal = gaussians.get_normal
    cov_inv = gaussians.get_inverse_covariance()
    tbar = tqdm(range(iterations), desc="Training visibility shs")
    raytracer = RayTracer(means3D, scaling, rotation)
    visibility_shs_view = gaussians.get_features.transpose(1, 2)
    vis_sh_degree = np.sqrt(visibility_shs_view.shape[-1]) - 1
    rays_o = means3D
    for iteration in tbar:
        rays_d = torch.randn_like(rays_o)
        rays_d = F.normalize(rays_d, dim=-1)
        mask = (rays_d * normal).sum(-1) < 0
        rays_d[mask] *= -1
        sample_sh2vis = eval_sh(vis_sh_degree, visibility_shs_view, rays_d)
        sample_vis = torch.clamp(sample_sh2vis + 0.5, 0.0, 1.0)
        trace_results = raytracer.trace_visibility(
            rays_o,
            rays_d,
            means3D,
            cov_inv,
            opacity,
            normal)
        visibility = trace_results["visibility"].repeat(3,1)
        loss = F.l1_loss(visibility, sample_vis)
        
        print(visibility.shape, sample_vis.shape)
        
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        
        
        if iteration % 1000 == 0:
            visibility = render(viewpoint_cam, gaussians, pipe, bg, override_color=sample_vis)['render']
            save_image(visibility, f"{iteration:05d}.png")


def prepare_output_and_logger(args):    
    if not args.model_path:
        args.model_path = os.path.join("./output/", args.output_name if args.output_name else args.source_path.split('/')[-1])
        
    # Set up output folder
    print("Output folder: {}".format(args.model_path))
    os.makedirs(args.model_path, exist_ok = True)
    with open(os.path.join(args.model_path, "cfg_args"), 'w') as cfg_log_f:
        cfg_log_f.write(str(Namespace(**vars(args))))

    # Create Tensorboard writer
    tb_writer = None
    if TENSORBOARD_FOUND:
        tb_writer = SummaryWriter(args.model_path)
    else:
        print("Tensorboard not available: not logging progress")
    return tb_writer

def training_report(tb_writer, iteration, Ll1, loss, l1_loss, elapsed, testing_iterations, scene : Scene, renderFunc, renderArgs):
    if tb_writer:
        tb_writer.add_scalar('train_loss_patches/l1_loss', Ll1.item(), iteration)
        tb_writer.add_scalar('train_loss_patches/total_loss', loss.item(), iteration)
        tb_writer.add_scalar('iter_time', elapsed, iteration)

    # Report test and samples of training set
    if iteration in testing_iterations:
        torch.cuda.empty_cache()
        validation_configs = ({'name': 'test', 'cameras' : scene.getTestCameras()}, 
                              {'name': 'train', 'cameras' : [scene.getTrainCameras()[idx % len(scene.getTrainCameras())] for idx in range(5, 30, 5)]})

        for config in validation_configs:
            if config['cameras'] and len(config['cameras']) > 0:
                l1_test = 0.0
                psnr_test = 0.0
                for idx, viewpoint in enumerate(config['cameras']):
                    image = torch.clamp(renderFunc(viewpoint, scene.gaussians, *renderArgs)["render"], 0.0, 1.0)
                    gt_image = torch.clamp(viewpoint.original_image.to("cuda"), 0.0, 1.0)
                    if tb_writer and (idx < 5):
                        tb_writer.add_images(config['name'] + "_view_{}/render".format(viewpoint.image_name), image[None], global_step=iteration)
                        if iteration == testing_iterations[0]:
                            tb_writer.add_images(config['name'] + "_view_{}/ground_truth".format(viewpoint.image_name), gt_image[None], global_step=iteration)
                    l1_test += l1_loss(image, gt_image).mean().double()
                    psnr_test += psnr(image, gt_image).mean().double()
                psnr_test /= len(config['cameras'])
                l1_test /= len(config['cameras'])          
                print("\n[ITER {}] Evaluating {}: L1 {} PSNR {}".format(iteration, config['name'], l1_test, psnr_test))
                if tb_writer:
                    tb_writer.add_scalar(config['name'] + '/loss_viewpoint - l1_loss', l1_test, iteration)
                    tb_writer.add_scalar(config['name'] + '/loss_viewpoint - psnr', psnr_test, iteration)

        if tb_writer:
            tb_writer.add_histogram("scene/opacity_histogram", scene.gaussians.get_opacity, iteration)
            tb_writer.add_scalar('total_points', scene.gaussians.get_xyz.shape[0], iteration)
        torch.cuda.empty_cache()

if __name__ == "__main__":
    # Set up command line argument parser
    parser = ArgumentParser(description="Training script parameters")
    lp = ModelParams(parser)
    op = OptimizationParams(parser)
    pp = PipelineParams(parser)
    parser.add_argument('--ip', type=str, default="127.0.0.1")
    parser.add_argument('--port', type=int, default=6009)
    parser.add_argument('--debug_from', type=int, default=-1)
    parser.add_argument('--detect_anomaly', action='store_true', default=False)
    parser.add_argument("--test_iterations", nargs="+", type=int, default=[7_000, 30_000])
    parser.add_argument("--save_iterations", nargs="+", type=int, default=[7_000, 30_000])
    parser.add_argument("--quiet", action="store_true")
    parser.add_argument("--checkpoint_iterations", nargs="+", type=int, default=[])
    parser.add_argument("--start_checkpoint", type=str, default = None)
    args = parser.parse_args(sys.argv[1:])
    args.save_iterations.append(args.iterations)
    
    print("Optimizing " + args.model_path)

    # Initialize system state (RNG)
    safe_state(args.quiet)

    # Start GUI server, configure and run training
    # network_gui.init(args.ip, args.port)
    torch.autograd.set_detect_anomaly(args.detect_anomaly)
    training(lp.extract(args), op.extract(args), pp.extract(args), args.test_iterations, args.save_iterations, args.checkpoint_iterations, args.start_checkpoint, args.debug_from)

    # All done
    print("\nTraining complete.")
